Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 62812

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-12T11:10:41+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

その他のオープンデータ。

• 新型コロナウイルス対策病床オープンデータ

　

Data Wrangling

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	113732
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	112012	0
dateAnnounced	0	1.00	10	10	0	289	0
gender	18921	0.83	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	109716	0.04	8	23	0	8	0
notes	59190	0.48	1	270	0	51744	1
mhlwPatientNumber	113283	0.00	1	11	0	434	0
prefecturePatientNumber	16578	0.85	5	20	0	97145	0
prefectureSourceURL	82421	0.28	5	224	0	3441	0
residence	26969	0.76	1	38	0	1423	0
sourceURL	637	0.99	1	239	0	8310	0
relatedPatients	102888	0.10	2	259	0	6549	0
knownCluster	111233	0.02	3	88	0	231	0
detectedCityTown	87132	0.23	2	22	0	663	0
cityPrefectureNumber	87393	0.23	1	34	0	26330	2
citySourceURL	101830	0.10	9	317	0	3654	0
deceasedDate	111875	0.02	10	10	0	238	0
deceasedReportedDate	112515	0.01	10	62	0	206	0
deathSourceURL	112660	0.01	14	123	0	653	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.98	TRU: 112011, FAL: 1721
charterFlightPassenger	113718	0	1.00	TRU: 14
cruisePassengerDisembarked	113721	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	31.48	23.93	-1	20	30	50	100	▅▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`) %>% 
  dplyr::rename(Pref = `都道府県`, region = `八地方区分`) %>%
  dplyr::filter(date != dplyr::if_else(lubridate::hour(lubridate::now()) < 17L,
                                       max(date), max(date) + 1))

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	112009
Number of columns	19
_______________________
Column type frequency:
character	3
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	112009	0
pref	0	1.00	3	15	0	49	0
knownCluster	109539	0.02	3	88	0	229	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-11	2020-08-16	288
deceasedDate	111630	0	2020-02-13	2020-10-17	2020-05-08	150
deceasedReportedDate	111679	0	2020-02-13	2020-10-17	2020-05-16	131

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	18222	0.84	FALSE	2	M: 52675, F: 41112
patientStatus	109476	0.02	FALSE	8	Hos: 1261, Dec: 371, Hom: 315, Dis: 283
ageBracket	0	1.00	FALSE	13	20: 25601, -1: 18321, 30: 16290, 40: 13559
pcode	1302	0.99	FALSE	47	13: 33415, 27: 14376, 14: 9619, 23: 7109
Pref	1302	0.99	FALSE	47	東京都: 33415, 大阪府: 14376, 神奈川: 9619, 愛知県: 7109
region	1302	0.99	FALSE	8	関東地: 57312, 近畿地: 22556, 九州地: 11392, 中部地: 11060
広域圏	9475	0.92	FALSE	8	首都圏: 57579, 近畿圏: 21954, 中部圏: 9661, 九州圏: 7792
通俗的区分	1302	0.99	FALSE	11	関東: 57312, 関西: 21954, 東海: 9269, 九州: 7792
fct_pref	1302	0.99	FALSE	47	Tok: 33415, Osa: 14376, Kan: 9619, Aic: 7109

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 112009
charterFlightPassenger	111995	0	1.00	TRU: 14
cruisePassengerDisembarked	111998	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 109539, TRU: 2470

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合はサマリデータを使った方がいい。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

集計（陽性者）

　

地方別集計

地方別で陽性者数と人口千人あたりの比率を比較する。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_region

　

都道府県別集計

同様に都道府県別に比較する。

r_by_prefs <- x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_prefs %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

累計陽性者数トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = n, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

人口千人あたりの比率トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = rate, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

クラスタ比率

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1))

　

x %>% 
  dplyr::group_by(Pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

日次集計（陽性者）

　

全国日次集計

日次単位の陽性者数、前日差、累計、移動平均（7日）を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n),
                ma7 = zoo::rollmeanr(n, k = 7L, na.pad = TRUE))

x_by_all

　

地方区分別日次集計

同様に地方区分別の集計を求める。

x_by_region <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::select(date, region, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_region

　

都道府県別日次集計

同様に都道府県別の集計を求める。

x_by_prefs <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "Pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_prefs

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, Pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(Pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "Pref", values_to = "n") %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = Pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_prefs, ., by = "Pref") %>% 
  dplyr::select(Pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の陽性者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の陽性者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

陽性者数（単日）

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("地方別陽性者数推移（単日） @", datetime),
                  caption = caption, 
                  x = "", y = "陽性者数") 

　

移動平均

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("地方別7日間移動平均 @", datetime),
                  caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 3) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

累積

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line() +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("地方別累積陽性者数 @", datetime),
                  caption = caption,
                  x = "", y = "累積陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)))

　

単日＋累積

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_prefs %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 203.6868 217.2235 242.1957 241.5979 262.5994 259.6396 273.0145 286.9297
##  [9] 295.7085 316.5340 320.2147 335.7612 340.2592 353.9357
## 
## $北海道地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 194.0923 189.0133
## 44.28571 206.7302 201.1754
## 44.42857 228.7867 221.6884
## 44.57143 226.7067 218.8237
## 44.71429 245.5122 236.4668
## 44.85714 240.8159 230.8513
## 45.00000 252.1835 241.1562
## 45.14286 262.8531 250.1077
## 45.28571 269.2248 255.2052
## 45.42857 287.1881 271.6534
## 45.57143 288.2882 271.3873
## 45.71429 301.0715 282.7078
## 45.85714 302.8916 283.1104
## 46.00000 313.8036 292.5589
## 
## $北海道地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 213.2812 218.3602
## 44.28571 227.7167 233.2715
## 44.42857 255.6047 262.7030
## 44.57143 256.4892 264.3722
## 44.71429 279.6865 288.7319
## 44.85714 278.4632 288.4279
## 45.00000 293.8456 304.8729
## 45.14286 311.0063 323.7517
## 45.28571 322.1923 336.2119
## 45.42857 345.8798 361.4146
## 45.57143 352.1412 369.0421
## 45.71429 370.4510 388.8146
## 45.85714 377.6267 397.4079
## 46.00000 394.0679 415.3126
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 30.86175 24.15146 21.81457 25.51928 24.10743 25.88013 25.07572 25.93740
##  [9] 25.49457 25.91882 25.68026 25.89129 25.76461 25.87041
## 
## $東北地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 24.26622 20.77476
## 44.28571 17.17318 13.47910
## 44.42857 14.77011 11.04100
## 44.57143 18.47439 14.74505
## 44.71429 16.81373 12.95268
## 44.85714 18.46942 14.54643
## 45.00000 17.35492 13.26777
## 45.14286 18.00411 13.80449
## 45.28571 17.25263 12.88961
## 45.42857 17.42784 12.93299
## 45.57143 16.89837 12.24952
## 45.71429 16.85335 12.06896
## 45.85714 16.45444 11.52593
## 46.00000 16.30881 11.24721
## 
## $東北地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 37.45729 40.94875
## 44.28571 31.12974 34.82381
## 44.42857 28.85902 32.58813
## 44.57143 32.56418 36.29352
## 44.71429 31.40113 35.26219
## 44.85714 33.29083 37.21382
## 45.00000 32.79653 36.88368
## 45.14286 33.87068 38.07031
## 45.28571 33.73651 38.09953
## 45.42857 34.40979 38.90464
## 45.57143 34.46216 39.11101
## 45.71429 34.92923 39.71362
## 45.85714 35.07478 40.00328
## 46.00000 35.43201 40.49361
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 713.1750 678.7665 662.2902 519.0469 424.4715 609.2814 635.3740 729.5130
##  [9] 693.8954 697.2523 534.6851 441.1638 606.4978 640.0344
## 
## $関東地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 649.6976 616.0947
## 44.28571 598.9306 556.6680
## 44.42857 574.2120 527.5862
## 44.57143 427.9486 379.7241
## 44.71429 330.0665 280.0914
## 44.85714 510.9980 458.9700
## 45.00000 530.3901 474.8149
## 45.14286 612.0471 549.8645
## 45.28571 566.2477 498.6751
## 45.42857 563.2992 492.3886
## 45.57143 396.5540 323.4317
## 45.71429 299.9269 225.1605
## 45.85714 461.8489 385.2764
## 46.00000 491.4527 412.7983
## 
## $関東地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 776.6524 810.2553
## 44.28571 758.6024 800.8649
## 44.42857 750.3684 796.9942
## 44.57143 610.1452 658.3698
## 44.71429 518.8766 568.8516
## 44.85714 707.5648 759.5928
## 45.00000 740.3580 795.9331
## 45.14286 846.9789 909.1615
## 45.28571 821.5431 889.1157
## 45.42857 831.2055 902.1160
## 45.57143 672.8163 745.9386
## 45.71429 582.4008 657.1672
## 45.85714 751.1467 827.7192
## 46.00000 788.6161 867.2705
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 179.2546 197.5771 212.6336 185.9267 178.2757 205.6376 215.8504 206.8644
##  [9] 226.8917 246.2443 213.1323 224.8192 240.4076 241.8606
## 
## $中部地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 160.9887 151.3193
## 44.28571 174.9362 162.9508
## 44.42857 186.2300 172.2528
## 44.57143 155.5117 139.4109
## 44.71429 145.3433 127.9099
## 44.85714 169.4963 150.3642
## 45.00000 177.1434 156.6532
## 45.14286 162.6065 139.1778
## 45.28571 177.8309 151.8597
## 45.42857 193.6383 165.7904
## 45.57143 156.3930 126.3570
## 45.71429 164.6794 132.8433
## 45.85714 177.1089 143.6005
## 46.00000 175.0750 139.7209
## 
## $中部地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 197.5206 207.1900
## 44.28571 220.2180 232.2033
## 44.42857 239.0372 253.0144
## 44.57143 216.3418 232.4426
## 44.71429 211.2082 228.6416
## 44.85714 241.7789 260.9110
## 45.00000 254.5574 275.0476
## 45.14286 251.1223 274.5510
## 45.28571 275.9525 301.9237
## 45.42857 298.8503 326.6982
## 45.57143 269.8717 299.9077
## 45.71429 284.9590 316.7951
## 45.85714 303.7064 337.2147
## 46.00000 308.6461 344.0002
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 353.6048 370.4710 390.4053 337.3569 294.7572 407.3487 408.9293 416.9355
##  [9] 448.2634 452.7833 411.5285 366.6317 475.9097 490.4423
## 
## $近畿地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 320.9846 303.7166
## 44.28571 332.1679 311.8915
## 44.42857 349.3767 327.6575
## 44.57143 292.1994 268.2946
## 44.71429 246.9670 221.6685
## 44.85714 356.2326 329.1734
## 45.00000 355.3058 326.9192
## 45.14286 355.1950 322.5115
## 45.28571 381.8459 346.6866
## 45.42857 382.4124 345.1603
## 45.57143 337.1110 297.7168
## 45.71429 288.6011 247.2942
## 45.85714 394.2601 351.0374
## 46.00000 405.4510 360.4593
## 
## $近畿地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 386.2250 403.4930
## 44.28571 408.7740 429.0504
## 44.42857 431.4339 453.1531
## 44.57143 382.5143 406.4191
## 44.71429 342.5474 367.8459
## 44.85714 458.4648 485.5240
## 45.00000 462.5529 490.9395
## 45.14286 478.6760 511.3595
## 45.28571 514.6808 549.8401
## 45.42857 523.1542 560.4064
## 45.57143 485.9460 525.3403
## 45.71429 444.6623 485.9692
## 45.85714 557.5593 600.7820
## 46.00000 575.4336 620.4253
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 11.28291 11.30022 11.30022 11.30022 11.30022 11.30022 11.30022 11.30022
##  [9] 11.30022 11.30022 11.30022 11.30022 11.30022 11.30022
## 
## $中国地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                  80%       95%
## 44.14286  2.30079294 -2.454053
## 44.28571  1.91422343 -3.054424
## 44.42857  1.72862061 -3.338279
## 44.57143  1.54654900 -3.616734
## 44.71429  1.36781440 -3.890085
## 44.85714  1.19223977 -4.158603
## 45.00000  1.01966323 -4.422536
## 45.14286  0.84993624 -4.682111
## 45.28571  0.68292214 -4.937537
## 45.42857  0.51849486 -5.189007
## 45.57143  0.35653779 -5.436699
## 45.71429  0.19694285 -5.680778
## 45.85714  0.03960959 -5.921399
## 46.00000 -0.11555549 -6.158703
## 
## $中国地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 20.26503 25.01988
## 44.28571 20.68622 25.65487
## 44.42857 20.87182 25.93872
## 44.57143 21.05390 26.21718
## 44.71429 21.23263 26.49053
## 44.85714 21.40821 26.75905
## 45.00000 21.58078 27.02298
## 45.14286 21.75051 27.28256
## 45.28571 21.91752 27.53798
## 45.42857 22.08195 27.78945
## 45.57143 22.24391 28.03714
## 45.71429 22.40350 28.28122
## 45.85714 22.56084 28.52184
## 46.00000 22.71600 28.75915
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.632834 1.623419 1.623419 1.623419 1.623419 1.623419 1.623419 1.623419
##  [9] 1.623419 1.623419 1.623419 1.623419 1.623419 1.623419
## 
## $四国地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -1.836508 -3.673067
## 44.28571 -2.099727 -4.070642
## 44.42857 -2.170838 -4.179396
## 44.57143 -2.240640 -4.286149
## 44.71429 -2.309203 -4.391008
## 44.85714 -2.376592 -4.494070
## 45.00000 -2.442864 -4.595423
## 45.14286 -2.508072 -4.695152
## 45.28571 -2.572268 -4.793330
## 45.42857 -2.635496 -4.890029
## 45.57143 -2.697799 -4.985313
## 45.71429 -2.759216 -5.079243
## 45.85714 -2.819785 -5.171874
## 46.00000 -2.879538 -5.263260
## 
## $四国地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 5.102176 6.938735
## 44.28571 5.346565 7.317479
## 44.42857 5.417675 7.426234
## 44.57143 5.487478 7.532987
## 44.71429 5.556041 7.637846
## 44.85714 5.623430 7.740907
## 45.00000 5.689701 7.842261
## 45.14286 5.754910 7.941989
## 45.28571 5.819106 8.040168
## 45.42857 5.882334 8.136867
## 45.57143 5.944636 8.232151
## 45.71429 6.006054 8.326081
## 45.85714 6.066622 8.418712
## 46.00000 6.126376 8.510098
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 71.56415 67.37508 58.57192 63.51328 59.20097 68.97590 71.49956 74.67140
##  [9] 73.59276 68.85777 71.29044 68.94078 72.73198 75.84457
## 
## $九州地方$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%        95%
## 44.14286 49.626231  38.012998
## 44.28571 40.813711  26.752973
## 44.42857 28.846073  13.110163
## 44.57143 32.850279  16.618267
## 44.71429 26.506740   9.199463
## 44.85714 32.086504  12.558435
## 45.00000 30.845609   9.324710
## 45.14286 27.811729   3.005719
## 45.28571 22.106750  -5.148298
## 45.42857 13.303183 -16.105640
## 45.57143 12.505714 -18.613043
## 45.71429  6.749262 -26.172941
## 45.85714  6.787661 -28.121153
## 46.00000  6.349813 -30.438490
## 
## $九州地方$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%      95%
## 44.14286  93.50207 105.1153
## 44.28571  93.93645 107.9972
## 44.42857  88.29776 104.0337
## 44.57143  94.17628 110.4083
## 44.71429  91.89519 109.2025
## 44.85714 105.86530 125.3934
## 45.00000 112.15350 133.6744
## 45.14286 121.53106 146.3371
## 45.28571 125.07877 152.3338
## 45.42857 124.41236 153.8212
## 45.57143 130.07517 161.1939
## 45.71429 131.13231 164.0545
## 45.85714 138.67630 173.5851
## 46.00000 145.33933 182.1276

　

都道府県別

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 203.6868 217.2235 242.1957 241.5979 262.5994 259.6396 273.0145 286.9297
##  [9] 295.7085 316.5340 320.2147 335.7612 340.2592 353.9357
## 
## $北海道$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 194.0923 189.0133
## 44.28571 206.7302 201.1754
## 44.42857 228.7867 221.6884
## 44.57143 226.7067 218.8237
## 44.71429 245.5122 236.4668
## 44.85714 240.8159 230.8513
## 45.00000 252.1835 241.1562
## 45.14286 262.8531 250.1077
## 45.28571 269.2248 255.2052
## 45.42857 287.1881 271.6534
## 45.57143 288.2882 271.3873
## 45.71429 301.0715 282.7078
## 45.85714 302.8916 283.1104
## 46.00000 313.8036 292.5589
## 
## $北海道$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 213.2812 218.3602
## 44.28571 227.7167 233.2715
## 44.42857 255.6047 262.7030
## 44.57143 256.4892 264.3722
## 44.71429 279.6865 288.7319
## 44.85714 278.4632 288.4279
## 45.00000 293.8456 304.8729
## 45.14286 311.0063 323.7517
## 45.28571 322.1923 336.2119
## 45.42857 345.8798 361.4146
## 45.57143 352.1412 369.0421
## 45.71429 370.4510 388.8146
## 45.85714 377.6267 397.4079
## 46.00000 394.0679 415.3126
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.4911346 1.9367078 1.0385411 1.5965920 1.2498626 1.4652932 1.3314414
##  [8] 1.4146065 1.3629342 1.3950393 1.3750917 1.3874856 1.3797850 1.3845695
## 
## $青森県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -2.059348 -3.409492
## 44.28571 -1.060818 -2.647613
## 44.42857 -2.035873 -3.663369
## 44.57143 -1.717436 -3.471776
## 44.71429 -2.182383 -3.999304
## 44.85714 -2.141026 -4.050096
## 45.00000 -2.404100 -4.381576
## 45.14286 -2.467486 -4.522541
## 45.28571 -2.647209 -4.770051
## 45.42857 -2.747056 -4.939748
## 45.57143 -2.890113 -5.147976
## 45.71429 -3.000244 -5.322968
## 45.85714 -3.125400 -5.510301
## 46.00000 -3.236139 -5.682194
## 
## $青森県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 3.041617 4.391761
## 44.28571 4.934234 6.521028
## 44.42857 4.112955 5.740452
## 44.57143 4.910620 6.664960
## 44.71429 4.682108 6.499030
## 44.85714 5.071613 6.980683
## 45.00000 5.066983 7.044459
## 45.14286 5.296699 7.351754
## 45.28571 5.373077 7.495919
## 45.42857 5.537134 7.729827
## 45.57143 5.640296 7.898159
## 45.71429 5.775216 8.097940
## 45.85714 5.884970 8.269871
## 46.00000 6.005278 8.451333
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.749323 1.999999 1.752190 1.752357 1.987069 2.112315 3.630057 2.163657
##  [9] 2.163657 2.163657 2.163657 2.163657 2.163657 2.163657
## 
## $岩手県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 0.9297778 0.4959371
## 44.28571 1.1700116 0.7306428
## 44.42857 0.9118891 0.4670609
## 44.57143 0.9018684 0.4516470
## 44.71429 1.1265136 0.6709629
## 44.85714 1.2418079 0.7809895
## 45.00000 2.7497119 2.2836853
## 45.14286 1.2257192 0.7292050
## 45.28571 1.2118230 0.7079525
## 45.42857 1.1981267 0.6870058
## 45.57143 1.1846220 0.6663522
## 45.71429 1.1713010 0.6459796
## 45.85714 1.1581565 0.6258768
## 46.00000 1.1451817 0.6060335
## 
## $岩手県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.568867 3.002708
## 44.28571 2.829987 3.269355
## 44.42857 2.592490 3.037318
## 44.57143 2.602845 3.053067
## 44.71429 2.847625 3.303176
## 44.85714 2.982821 3.443640
## 45.00000 4.510402 4.976429
## 45.14286 3.101595 3.598109
## 45.28571 3.115491 3.619361
## 45.42857 3.129187 3.640308
## 45.57143 3.142692 3.660962
## 45.71429 3.156013 3.681334
## 45.85714 3.169157 3.701437
## 46.00000 3.182132 3.721280
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 19.04768 21.64703 19.58236 21.22234 19.91970 20.95439 20.13253 20.78533
##  [9] 20.26681 20.67867 20.35153 20.61138 20.40498 20.56893
## 
## $宮城県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 14.31003 11.80207
## 44.28571 16.85246 14.31436
## 44.42857 14.44801 11.73005
## 44.57143 15.99847 13.23312
## 44.71429 14.43933 11.53820
## 44.85714 15.36359 12.40400
## 45.00000 14.33256 11.26224
## 45.14286 14.86338 11.72848
## 45.28571 14.16464 10.93434
## 45.42857 14.44927 11.15162
## 45.57143 13.96079 10.57773
## 45.71429 14.09205 10.64092
## 45.85714 13.73735 10.20772
## 46.00000 13.77362 10.17640
## 
## $宮城県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 23.78533 26.29329
## 44.28571 26.44161 28.97970
## 44.42857 24.71671 27.43467
## 44.57143 26.44620 29.21155
## 44.71429 25.40006 28.30119
## 44.85714 26.54518 29.50477
## 45.00000 25.93250 29.00282
## 45.14286 26.70729 29.84218
## 45.28571 26.36898 29.59927
## 45.42857 26.90808 30.20573
## 45.57143 26.74227 30.12532
## 45.71429 27.13071 30.58184
## 45.85714 27.07261 30.60224
## 46.00000 27.36424 30.96145
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.1795580 0.2249226 0.2249226 0.2249226 0.2249226 0.2249226 0.2249226
##  [8] 0.2249226 0.2249226 0.2249226 0.2249226 0.2249226 0.2249226 0.2249226
## 
## $秋田県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -1.047510 -1.697081
## 44.28571 -1.013421 -1.668960
## 44.42857 -1.013421 -1.668960
## 44.57143 -1.013421 -1.668960
## 44.71429 -1.013421 -1.668960
## 44.85714 -1.013421 -1.668960
## 45.00000 -1.013421 -1.668960
## 45.14286 -1.013421 -1.668960
## 45.28571 -1.013421 -1.668960
## 45.42857 -1.013421 -1.668960
## 45.57143 -1.013421 -1.668960
## 45.71429 -1.013421 -1.668960
## 45.85714 -1.013421 -1.668960
## 46.00000 -1.013421 -1.668960
## 
## $秋田県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 1.406626 2.056197
## 44.28571 1.463266 2.118805
## 44.42857 1.463266 2.118805
## 44.57143 1.463266 2.118805
## 44.71429 1.463266 2.118805
## 44.85714 1.463266 2.118805
## 45.00000 1.463266 2.118805
## 45.14286 1.463266 2.118805
## 45.28571 1.463266 2.118805
## 45.42857 1.463266 2.118805
## 45.57143 1.463266 2.118805
## 45.71429 1.463266 2.118805
## 45.85714 1.463266 2.118805
## 46.00000 1.463266 2.118805
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.2446738 1.4591212 1.6095967 1.5098480 1.5602119 1.6115841 1.2012535
##  [8] 1.5403388 1.4572418 1.3666549 1.2713076 1.1736094 1.0756542 0.9792298
## 
## $山形県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                  80%         95%
## 44.14286  0.30275965 -0.19585959
## 44.28571  0.49708308 -0.01218912
## 44.42857  0.61774178  0.09268550
## 44.57143  0.48033139 -0.06466179
## 44.71429  0.48776296 -0.07995723
## 44.85714  0.49357886 -0.09825738
## 45.00000  0.03740156 -0.57870443
## 45.14286  0.36927665 -0.25064623
## 45.28571  0.25352482 -0.38368445
## 45.42857  0.13540845 -0.51637403
## 45.57143  0.01734140 -0.64646820
## 45.71429 -0.09869670 -0.77221488
## 45.85714 -0.21110981 -0.89228154
## 46.00000 -0.31863901 -1.00568925
## 
## $山形県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.186588 2.685207
## 44.28571 2.421159 2.930431
## 44.42857 2.601452 3.126508
## 44.57143 2.539365 3.084358
## 44.71429 2.632661 3.200381
## 44.85714 2.729589 3.321426
## 45.00000 2.365105 2.981211
## 45.14286 2.711401 3.331324
## 45.28571 2.660959 3.298168
## 45.42857 2.597901 3.249684
## 45.57143 2.525274 3.189083
## 45.71429 2.445916 3.119434
## 45.85714 2.362418 3.043590
## 46.00000 2.277099 2.964149
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 2.223880 1.594911 1.802918 1.810811 1.954783 2.875967 1.703829 2.661640
##  [9] 1.926840 2.078907 2.019661 2.132257 1.660775 1.915530
## 
## $福島県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                  80%       95%
## 44.14286 -0.30601543 -1.645261
## 44.28571 -1.00736834 -2.384932
## 44.42857 -0.86978545 -2.284629
## 44.57143 -0.93050864 -2.381676
## 44.71429 -0.85347618 -2.340079
## 44.85714  0.00232645 -1.518887
## 45.00000 -1.23373723 -2.788791
## 45.14286 -0.39226804 -2.008909
## 45.28571 -1.19837792 -2.852768
## 45.42857 -1.11602948 -2.807327
## 45.57143 -1.24350476 -2.970920
## 45.71429 -1.19774015 -2.960534
## 45.85714 -1.73473848 -3.532215
## 46.00000 -1.54426011 -3.375762
## 
## $福島県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 4.753775 6.093021
## 44.28571 4.197190 5.574754
## 44.42857 4.475622 5.890466
## 44.57143 4.552131 6.003298
## 44.71429 4.763043 6.249646
## 44.85714 5.749607 7.270821
## 45.00000 4.641396 6.196450
## 45.14286 5.715548 7.332189
## 45.28571 5.052058 6.706448
## 45.42857 5.273843 6.965140
## 45.57143 5.282827 7.010242
## 45.71429 5.462255 7.225049
## 45.85714 5.056289 6.853766
## 46.00000 5.375320 7.206822
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 12.18885 11.89908 12.21491 12.42521 11.93628 12.13641 13.17843 12.37397
##  [9] 12.37397 12.37397 12.37397 12.37397 12.37397 12.37397
## 
## $茨城県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 8.662701 6.796067
## 44.28571 8.234746 6.294964
## 44.42857 8.417415 6.407144
## 44.57143 8.499072 6.420702
## 44.71429 7.885583 5.741275
## 44.85714 7.964873 5.756595
## 45.00000 8.889454 6.619008
## 45.14286 7.881945 5.504013
## 45.28571 7.751007 5.303760
## 45.42857 7.623677 5.109025
## 45.57143 7.499672 4.919376
## 45.71429 7.378744 4.734433
## 45.85714 7.260675 4.553862
## 46.00000 7.145272 4.377368
## 
## $茨城県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 15.71501 17.58164
## 44.28571 15.56342 17.50320
## 44.42857 16.01240 18.02267
## 44.57143 16.35134 18.42971
## 44.71429 15.98697 18.13128
## 44.85714 16.30795 18.51622
## 45.00000 17.46740 19.73785
## 45.14286 16.86599 19.24392
## 45.28571 16.99693 19.44417
## 45.42857 17.12426 19.63891
## 45.57143 17.24826 19.82856
## 45.71429 17.36919 20.01350
## 45.85714 17.48726 20.19407
## 46.00000 17.60266 20.37056
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.663398 1.831513 1.847253 1.551334 1.698896 2.243776 1.901455 1.838901
##  [9] 1.838901 1.838901 1.838901 1.838901 1.838901 1.838901
## 
## $栃木県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286 -1.3245193 -2.906227
## 44.28571 -1.1992450 -2.803631
## 44.42857 -1.2257485 -2.852497
## 44.57143 -1.5633375 -3.212145
## 44.71429 -1.4568958 -3.127471
## 44.85714 -0.9526071 -2.644670
## 45.00000 -1.3350107 -3.048292
## 45.14286 -1.5701623 -3.374811
## 45.28571 -1.6231677 -3.455876
## 45.42857 -1.6753737 -3.535718
## 45.57143 -1.7268154 -3.614392
## 45.71429 -1.7775254 -3.691946
## 45.85714 -1.8275342 -3.768428
## 46.00000 -1.8768700 -3.843880
## 
## $栃木県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 4.651316 6.233024
## 44.28571 4.862271 6.466657
## 44.42857 4.920254 6.547003
## 44.57143 4.666006 6.314814
## 44.71429 4.854688 6.525263
## 44.85714 5.440159 7.132222
## 45.00000 5.137920 6.851201
## 45.14286 5.247964 7.052613
## 45.28571 5.300970 7.133678
## 45.42857 5.353176 7.213520
## 45.57143 5.404617 7.292194
## 45.71429 5.455327 7.369748
## 45.85714 5.505336 7.446230
## 46.00000 5.554672 7.521682
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 3.935765 4.603118 5.204826 4.824194 4.799824 5.235210 5.328211 5.304255
##  [9] 5.340455 5.457814 5.489849 5.516776 5.533928 5.575389
## 
## $群馬県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286 -0.5402689 -2.909738
## 44.28571 -0.5233625 -3.237157
## 44.42857 -0.1223041 -2.942317
## 44.57143 -0.5664499 -3.420084
## 44.71429 -0.9118802 -3.935474
## 44.85714 -0.5747208 -3.650312
## 45.00000 -0.5932327 -3.727855
## 45.14286 -0.7240416 -3.915229
## 45.28571 -0.8288427 -4.094672
## 45.42857 -0.7625328 -4.055385
## 45.57143 -0.7962062 -4.123843
## 45.71429 -0.8117961 -4.161940
## 45.85714 -0.8500422 -4.229512
## 46.00000 -0.8390567 -4.234659
## 
## $群馬県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%      95%
## 44.14286  8.411799 10.78127
## 44.28571  9.729598 12.44339
## 44.42857 10.531957 13.35197
## 44.57143 10.214838 13.06847
## 44.71429 10.511528 13.53512
## 44.85714 11.045141 14.12073
## 45.00000 11.249654 14.38428
## 45.14286 11.332551 14.52374
## 45.28571 11.509753 14.77558
## 45.42857 11.678160 14.97101
## 45.57143 11.775904 15.10354
## 45.71429 11.845348 15.19549
## 45.85714 11.917898 15.29737
## 46.00000 11.989834 15.38544
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1]  78.21259 109.50297  89.95140  79.18192  80.46423  86.86143 107.76227
##  [8]  92.71294 117.63618 102.06308  93.48503  94.50641  99.60187 116.24969
## 
## $埼玉県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 64.64001 57.45512
## 44.28571 95.21082 87.64501
## 44.42857 74.97422 67.04577
## 44.57143 63.54968 55.27447
## 44.71429 64.20331 55.59529
## 44.85714 69.99524 61.06682
## 45.00000 90.31179 81.07407
## 45.14286 72.98096 62.53548
## 45.28571 97.00071 86.07696
## 45.42857 80.56205 69.18010
## 45.57143 71.15196 59.32955
## 45.71429 71.37121 59.12418
## 45.85714 75.69144 63.03402
## 46.00000 91.58838 78.53347
## 
## $埼玉県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286  91.78516  98.97005
## 44.28571 123.79512 131.36092
## 44.42857 104.92859 112.85704
## 44.57143  94.81416 103.08936
## 44.71429  96.72515 105.33316
## 44.85714 103.72762 112.65604
## 45.00000 125.21274 134.45046
## 45.14286 112.44492 122.89040
## 45.28571 138.27166 149.19541
## 45.42857 123.56411 134.94607
## 45.57143 115.81809 127.64050
## 45.71429 117.64160 129.88864
## 45.85714 123.51231 136.16972
## 46.00000 140.91101 153.96592
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 58.71429 49.71490 54.38043 50.12897 47.71792 51.72304 54.68624 55.48024
##  [9] 52.71884 54.94466 53.27073 51.99854 53.34888 55.67343
## 
## $千葉県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 47.21402 41.12615
## 44.28571 36.91609 30.14081
## 44.42857 41.19213 34.21066
## 44.57143 36.56235 29.38062
## 44.71429 33.78325 26.40668
## 44.85714 37.42979 29.86341
## 45.00000 40.04320 32.29164
## 45.14286 39.81530 31.52278
## 45.28571 36.46226 27.85654
## 45.42857 38.24049 29.39783
## 45.57143 36.13066 27.05725
## 45.71429 34.43338 25.13495
## 45.85714 35.36868 25.85054
## 46.00000 37.28755 27.55466
## 
## $千葉県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 70.21455 76.30242
## 44.28571 62.51371 69.28899
## 44.42857 67.56874 74.55020
## 44.57143 63.69559 70.87733
## 44.71429 61.65259 69.02916
## 44.85714 66.01628 73.58267
## 45.00000 69.32928 77.08083
## 45.14286 71.14519 79.43771
## 45.28571 68.97543 77.58115
## 45.42857 71.64883 80.49149
## 45.57143 70.41081 79.48422
## 45.71429 69.56370 78.86214
## 45.85714 71.32909 80.84723
## 46.00000 74.05930 83.79220
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 361.5793 316.6987 347.3760 246.1041 205.3954 307.1901 293.5067 366.8972
##  [9] 330.5567 360.7848 259.0784 217.9491 319.3370 305.2598
## 
## $東京都$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%       95%
## 44.14286 316.0073 291.88298
## 44.28571 264.1656 236.35622
## 44.42857 291.8124 262.39884
## 44.57143 187.8460 157.00606
## 44.71429 144.7229 112.60491
## 44.85714 244.3414 211.07126
## 45.00000 228.6865 194.37283
## 45.14286 295.1313 257.14071
## 45.28571 254.9746 214.96392
## 45.42857 282.4700 241.01266
## 45.57143 178.2888 135.52134
## 45.71429 134.9094  90.95076
## 45.85714 234.2445 189.19930
## 46.00000 218.2894 172.25008
## 
## $東京都$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 407.1512 431.2755
## 44.28571 369.2318 397.0412
## 44.42857 402.9396 432.3532
## 44.57143 304.3622 335.2022
## 44.71429 266.0678 298.1858
## 44.85714 370.0389 403.3090
## 45.00000 358.3268 392.6405
## 45.14286 438.6631 476.6537
## 45.28571 406.1387 446.1494
## 45.42857 439.0997 480.5570
## 45.57143 339.8680 382.6354
## 45.71429 300.9888 344.9474
## 45.85714 404.4295 449.4747
## 46.00000 392.2302 438.2696
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 127.24667 109.55143 120.38947  94.51059  77.60272 101.49189 115.37534
##  [8] 113.99680 108.61934 112.47630 100.19124  94.70791 103.37466 112.24348
## 
## $神奈川県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%      95%
## 44.14286 107.12090 96.46696
## 44.28571  87.18583 75.34620
## 44.42857  97.26703 85.02675
## 44.57143  70.97143 58.51055
## 44.71429  53.74869 41.12113
## 44.85714  77.35900 64.58382
## 45.00000  90.97818 78.06310
## 45.14286  86.82522 72.44145
## 45.28571  80.40012 65.46177
## 45.42857  83.62853 68.35744
## 45.57143  70.84637 55.31213
## 45.71429  64.91464 49.14303
## 45.85714  73.15414 57.15636
## 46.00000  81.60697 65.38898
## 
## $神奈川県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 147.3724 158.0264
## 44.28571 131.9170 143.7567
## 44.42857 143.5119 155.7522
## 44.57143 118.0497 130.5106
## 44.71429 101.4568 114.0843
## 44.85714 125.6248 138.4000
## 45.00000 139.7725 152.6876
## 45.14286 141.1684 155.5521
## 45.28571 136.8386 151.7769
## 45.42857 141.3241 156.5952
## 45.57143 129.5361 145.0704
## 45.71429 124.5012 140.2728
## 45.85714 133.5952 149.5930
## 46.00000 142.8800 159.0980
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 5.589649 5.195631 6.665273 5.811007 3.822403 2.802500 4.860428 2.468550
##  [9] 2.166680 2.238702 1.779988 1.470161 1.260897 1.119555
## 
## $新潟県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                  80%        95%
## 44.14286  3.85020136  2.9293932
## 44.28571  3.41263811  2.4687787
## 44.42857  4.82392381  3.8491722
## 44.57143  3.84819033  2.8091380
## 44.71429  1.80660333  0.7395034
## 44.85714  0.76298695 -0.3166662
## 45.00000  2.81018710  1.7248554
## 45.14286  0.36578546 -0.7473508
## 45.28571  0.05714614 -1.0595734
## 45.42857  0.12379797 -0.9957643
## 45.57143 -0.34180365 -1.4650121
## 45.71429 -0.65476471 -1.7796325
## 45.85714 -0.86545732 -1.9910814
## 46.00000 -1.00745064 -2.1334195
## 
## $新潟県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 7.329097 8.249906
## 44.28571 6.978624 7.922483
## 44.42857 8.506623 9.481375
## 44.57143 7.773823 8.812876
## 44.71429 5.838203 6.905303
## 44.85714 4.842014 5.921667
## 45.00000 6.910668 7.996000
## 45.14286 4.571314 5.684451
## 45.28571 4.276213 5.392933
## 45.42857 4.353605 5.473167
## 45.57143 3.901779 5.024987
## 45.71429 3.595087 4.719955
## 45.85714 3.387252 4.512876
## 46.00000 3.246561 4.372530
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.2590860 0.2090667 0.3156261 0.1927936 0.2185731 0.1995299 0.2037810
##  [8] 0.1816158 0.1774537 0.1674317 0.1610192 0.1518065 0.1450626 0.1378293
## 
## $富山県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -2.211510 -3.519364
## 44.28571 -2.400332 -3.781664
## 44.42857 -2.453040 -3.918683
## 44.57143 -2.697917 -4.228167
## 44.71429 -2.861134 -4.491432
## 44.85714 -2.991984 -4.681469
## 45.00000 -3.092541 -4.837508
## 45.14286 -3.204181 -4.996513
## 45.28571 -3.293970 -5.131631
## 45.42857 -3.375683 -5.251295
## 45.57143 -3.446654 -5.356440
## 45.71429 -3.512998 -5.453029
## 45.85714 -3.571301 -5.538625
## 46.00000 -3.624253 -5.615779
## 
## $富山県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.729682 4.037536
## 44.28571 2.818465 4.199798
## 44.42857 3.084292 4.549936
## 44.57143 3.083504 4.613754
## 44.71429 3.298280 4.928578
## 44.85714 3.391043 5.080529
## 45.00000 3.500103 5.245070
## 45.14286 3.567413 5.359745
## 45.28571 3.648878 5.486539
## 45.42857 3.710547 5.586159
## 45.57143 3.768692 5.678479
## 45.71429 3.816611 5.756642
## 45.85714 3.861426 5.828750
## 46.00000 3.899911 5.891437
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.34674 1.34674 1.34674 1.34674 1.34674 1.34674 1.34674 1.34674 1.34674
## [10] 1.34674 1.34674 1.34674 1.34674 1.34674
## 
## $石川県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -2.351607 -4.309393
## 44.28571 -2.559638 -4.627550
## 44.42857 -2.757138 -4.929599
## 44.57143 -2.945559 -5.217765
## 44.71429 -3.126051 -5.493803
## 44.85714 -3.299536 -5.759125
## 45.00000 -3.466772 -6.014891
## 45.14286 -3.628390 -6.262065
## 45.28571 -3.784921 -6.501458
## 45.42857 -3.936816 -6.733762
## 45.57143 -4.084465 -6.959571
## 45.71429 -4.228205 -7.179402
## 45.85714 -4.368330 -7.393706
## 46.00000 -4.505102 -7.602879
## 
## $石川県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%       95%
## 44.14286 5.045087  7.002873
## 44.28571 5.253118  7.321030
## 44.42857 5.450618  7.623079
## 44.57143 5.639039  7.911245
## 44.71429 5.819530  8.187283
## 44.85714 5.993015  8.452605
## 45.00000 6.160252  8.708371
## 45.14286 6.321870  8.955545
## 45.28571 6.478401  9.194938
## 45.42857 6.630296  9.427241
## 45.57143 6.777945  9.653051
## 45.71429 6.921684  9.872882
## 45.85714 7.061810 10.087186
## 46.00000 7.198581 10.296359
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 2.986121 2.429858 1.864366 2.318643 1.825123 1.686713 1.822769 1.505617
##  [9] 1.499126 1.493223 1.320322 1.333876 1.285422 1.200349
## 
## $福井県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%        95%
## 44.14286  1.1530608  0.1826976
## 44.28571  0.4886560 -0.5389541
## 44.42857 -0.2828917 -1.4195814
## 44.57143 -0.0745660 -1.3414543
## 44.71429 -0.6128951 -1.9035042
## 44.85714 -0.8540407 -2.1990345
## 45.00000 -0.7910753 -2.1747610
## 45.14286 -1.1339256 -2.5312152
## 45.28571 -1.1880377 -2.6105364
## 45.42857 -1.2195213 -2.6555613
## 45.57143 -1.4075422 -2.8515861
## 45.71429 -1.4146418 -2.8696192
## 45.85714 -1.4733787 -2.9337994
## 46.00000 -1.5668227 -3.0316752
## 
## $福井県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 4.819180 5.789544
## 44.28571 4.371059 5.398669
## 44.42857 4.011624 5.148314
## 44.57143 4.711852 5.978740
## 44.71429 4.263142 5.553751
## 44.85714 4.227467 5.572461
## 45.00000 4.436614 5.820300
## 45.14286 4.145160 5.542450
## 45.28571 4.186290 5.608789
## 45.42857 4.205967 5.642007
## 45.57143 4.048186 5.492229
## 45.71429 4.082394 5.537371
## 45.85714 4.044222 5.504643
## 46.00000 3.967522 5.432374
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 8.386274 8.095170 8.095170 8.095170 8.095170 8.095170 8.095170 8.095170
##  [9] 8.095170 8.095170 8.095170 8.095170 8.095170 8.095170
## 
## $山梨県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 6.658971 5.744592
## 44.28571 6.206371 5.206501
## 44.42857 6.113782 5.064898
## 44.57143 6.025330 4.929623
## 44.71429 5.940506 4.799896
## 44.85714 5.858897 4.675086
## 45.00000 5.780164 4.554674
## 45.14286 5.704021 4.438224
## 45.28571 5.630230 4.325369
## 45.42857 5.558584 4.215796
## 45.57143 5.488907 4.109235
## 45.71429 5.421045 4.005448
## 45.85714 5.354863 3.904232
## 46.00000 5.290242 3.805403
## 
## $山梨県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%      95%
## 44.14286 10.113578 11.02796
## 44.28571  9.983969 10.98384
## 44.42857 10.076559 11.12544
## 44.57143 10.165011 11.26072
## 44.71429 10.249834 11.39044
## 44.85714 10.331443 11.51525
## 45.00000 10.410177 11.63567
## 45.14286 10.486319 11.75212
## 45.28571 10.560111 11.86497
## 45.42857 10.631756 11.97454
## 45.57143 10.701433 12.08111
## 45.71429 10.769296 12.18489
## 45.85714 10.835478 12.28611
## 46.00000 10.900099 12.38494
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 6.487625 6.343595 6.343595 6.343595 6.343595 6.343595 6.343595 6.343595
##  [9] 6.343595 6.343595 6.343595 6.343595 6.343595 6.343595
## 
## $長野県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%         95%
## 44.14286 3.748884  2.29908250
## 44.28571 3.530279  2.04099941
## 44.42857 3.374179  1.80226501
## 44.57143 3.225885  1.57546881
## 44.71429 3.084331  1.35898100
## 44.85714 2.948674  1.15151199
## 45.00000 2.818234  0.95202053
## 45.14286 2.692451  0.75965154
## 45.28571 2.570859  0.57369258
## 45.42857 2.453065  0.39354263
## 45.57143 2.338734  0.21868908
## 45.71429 2.227578  0.04869057
## 45.85714 2.119346 -0.11683618
## 46.00000 2.013819 -0.27822653
## 
## $長野県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%      95%
## 44.14286  9.226366 10.67617
## 44.28571  9.156911 10.64619
## 44.42857  9.313011 10.88493
## 44.57143  9.461305 11.11172
## 44.71429  9.602859 11.32821
## 44.85714  9.738516 11.53568
## 45.00000  9.868956 11.73517
## 45.14286  9.994740 11.92754
## 45.28571 10.116332 12.11350
## 45.42857 10.234125 12.29365
## 45.57143 10.348456 12.46850
## 45.71429 10.459612 12.63850
## 45.85714 10.567844 12.80403
## 46.00000 10.673372 12.96542
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 11.444956 12.133822  8.140248  8.105240  8.395724  9.540251  8.043596
##  [8]  8.528643  9.085887  6.378566  6.749723  6.413707  7.251279  6.672276
## 
## $岐阜県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%        95%
## 44.14286 8.1627671  6.4252813
## 44.28571 8.3799371  6.3927507
## 44.42857 3.9621786  1.7504422
## 44.57143 3.6719878  1.3251658
## 44.71429 3.7774218  1.3326405
## 44.85714 4.7914747  2.2776243
## 45.00000 3.1993992  0.6350362
## 45.14286 3.3752195  0.6471621
## 45.28571 3.7855264  0.9796849
## 45.42857 0.9539816 -1.9176201
## 45.57143 1.2372309 -1.6809061
## 45.71429 0.8337276 -2.1201352
## 45.85714 1.6197972 -1.3613292
## 46.00000 1.0005669 -2.0018542
## 
## $岐阜県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 14.72714 16.46463
## 44.28571 15.88771 17.87489
## 44.42857 12.31832 14.53005
## 44.57143 12.53849 14.88531
## 44.71429 13.01403 15.45881
## 44.85714 14.28903 16.80288
## 45.00000 12.88779 15.45216
## 45.14286 13.68207 16.41012
## 45.28571 14.38625 17.19209
## 45.42857 11.80315 14.67475
## 45.57143 12.26221 15.18035
## 45.71429 11.99369 14.94755
## 45.85714 12.88276 15.86389
## 46.00000 12.34398 15.34641
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 17.10958 14.91644 16.35415 14.89202 14.01553 17.41010 15.01137 15.82845
##  [9] 17.69896 20.06241 16.54947 15.23595 17.06198 17.64283
## 
## $静岡県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 13.259915 11.222027
## 44.28571 10.446300  8.079950
## 44.42857 11.613839  9.104470
## 44.57143 10.002926  7.414799
## 44.71429  8.759618  5.977305
## 44.85714 11.907289  8.994276
## 45.00000  9.338267  6.335107
## 45.14286  9.933244  6.812510
## 45.28571 11.604417  8.378159
## 45.42857 13.804868 10.492325
## 45.57143 10.083572  6.660732
## 45.71429  8.585821  5.065455
## 45.85714 10.250423  6.644602
## 46.00000 10.650068  6.948323
## 
## $静岡県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 20.95924 22.99713
## 44.28571 19.38658 21.75294
## 44.42857 21.09446 23.60383
## 44.57143 19.78110 22.36923
## 44.71429 19.27144 22.05376
## 44.85714 22.91291 25.82593
## 45.00000 20.68448 23.68764
## 45.14286 21.72366 24.84439
## 45.28571 23.79351 27.01977
## 45.42857 26.31995 29.63250
## 45.57143 23.01537 26.43821
## 45.71429 21.88608 25.40645
## 45.85714 23.87354 27.47936
## 46.00000 24.63560 28.33734
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 106.4376 126.2109 125.7663 115.6519 110.3510 130.7555 134.7741 126.0761
##  [9] 135.0218 145.4504 125.4186 135.0164 141.2167 137.3970
## 
## $愛知県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286  92.37989  84.93817
## 44.28571 109.38432 100.47685
## 44.42857 105.95975  95.47479
## 44.57143  92.81745  80.72963
## 44.71429  85.68201  72.62303
## 44.85714 103.59611  89.21881
## 45.00000 105.72535  90.34786
## 45.14286  92.46865  74.67794
## 45.28571  97.77362  78.05563
## 45.42857 105.40348  84.20393
## 45.57143  82.07072  59.12373
## 45.71429  89.04417  64.70793
## 45.85714  92.74392  67.08397
## 46.00000  86.18852  59.08036
## 
## $愛知県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 120.4954 127.9371
## 44.28571 143.0375 151.9450
## 44.42857 145.5729 156.0578
## 44.57143 138.4863 150.5741
## 44.71429 135.0200 148.0790
## 44.85714 157.9148 172.2921
## 45.00000 163.8229 179.2004
## 45.14286 159.6836 177.4743
## 45.28571 172.2700 191.9880
## 45.42857 185.4973 206.6969
## 45.57143 168.7665 191.7135
## 45.71429 180.9887 205.3249
## 45.85714 189.6895 215.3495
## 46.00000 188.6056 215.7137
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 3.264528 3.264528 3.264528 3.264528 3.264528 3.264528 3.264528 3.264528
##  [9] 3.264528 3.264528 3.264528 3.264528 3.264528 3.264528
## 
## $三重県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286 -0.4456774 -2.409742
## 44.28571 -0.6511906 -2.724047
## 44.42857 -0.8464428 -3.022659
## 44.57143 -1.0328327 -3.307718
## 44.71429 -1.2114677 -3.580917
## 44.85714 -1.3832420 -3.843623
## 45.00000 -1.5488902 -4.096960
## 45.14286 -1.7090243 -4.341864
## 45.28571 -1.8641610 -4.579125
## 45.42857 -2.0147409 -4.809417
## 45.57143 -2.1611432 -5.033320
## 45.71429 -2.3036976 -5.251338
## 45.85714 -2.4426924 -5.463913
## 46.00000 -2.5783817 -5.671431
## 
## $三重県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%       95%
## 44.14286 6.974734  8.938798
## 44.28571 7.180247  9.253103
## 44.42857 7.375499  9.551716
## 44.57143 7.561889  9.836775
## 44.71429 7.740524 10.109973
## 44.85714 7.912298 10.372679
## 45.00000 8.077947 10.626016
## 45.14286 8.238081 10.870920
## 45.28571 8.393217 11.108182
## 45.42857 8.543797 11.338474
## 45.57143 8.690200 11.562377
## 45.71429 8.832754 11.780395
## 45.85714 8.971749 11.992969
## 46.00000 9.107438 12.200488
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 6.871620 8.803106 7.440293 8.060807 7.610295 7.809171 7.660010 7.723573
##  [9] 7.674102 7.694350 7.677912 7.684337 7.678863 7.680892
## 
## $滋賀県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%         95%
## 44.14286 3.020219  0.98141125
## 44.28571 4.888605  2.81639304
## 44.42857 3.079754  0.77142419
## 44.57143 3.614037  1.26005936
## 44.71429 2.989334  0.54314468
## 44.85714 3.100167  0.60737072
## 45.00000 2.840275  0.28886122
## 45.14286 2.818701  0.22221836
## 45.28571 2.678544  0.03405521
## 45.42857 2.616911 -0.07092291
## 45.57143 2.517833 -0.21374722
## 45.71429 2.445230 -0.32818536
## 45.85714 2.361298 -0.45365157
## 46.00000 2.286761 -0.56871930
## 
## $滋賀県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 10.72302 12.76183
## 44.28571 12.71761 14.78982
## 44.42857 11.80083 14.10916
## 44.57143 12.50758 14.86156
## 44.71429 12.23126 14.67745
## 44.85714 12.51818 15.01097
## 45.00000 12.47975 15.03116
## 45.14286 12.62845 15.22493
## 45.28571 12.66966 15.31415
## 45.42857 12.77179 15.45962
## 45.57143 12.83799 15.56957
## 45.71429 12.92344 15.69686
## 45.85714 12.99643 15.81138
## 46.00000 13.07502 15.93050
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 15.82111 17.57412 17.71811 17.46458 14.47494 15.96799 15.88652 15.88171
##  [9] 17.49544 16.90068 17.12233 15.51282 16.04954 16.71934
## 
## $京都府$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%      95%
## 44.14286  9.477345 6.119160
## 44.28571 10.659215 6.998683
## 44.42857 10.655220 6.916351
## 44.57143 10.256744 6.441146
## 44.71429  7.125020 3.234205
## 44.85714  8.478674 4.514070
## 45.00000  8.260357 4.223311
## 45.14286  7.877016 3.639586
## 45.28571  9.275326 4.923862
## 45.42857  8.513910 4.074224
## 45.57143  8.572152 4.045962
## 45.71429  6.802304 2.191233
## 45.85714  7.181578 2.487161
## 46.00000  7.696675 2.920366
## 
## $京都府$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 22.16487 25.52305
## 44.28571 24.48903 28.14957
## 44.42857 24.78100 28.51987
## 44.57143 24.67242 28.48802
## 44.71429 21.82487 25.71568
## 44.85714 23.45731 27.42191
## 45.00000 23.51268 27.54973
## 45.14286 23.88641 28.12384
## 45.28571 25.71555 30.06701
## 45.42857 25.28744 29.72713
## 45.57143 25.67250 30.19869
## 45.71429 24.22334 28.83441
## 45.85714 24.91751 29.61193
## 46.00000 25.74200 30.51831
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 216.2436 227.1502 237.3869 220.6051 192.9560 286.4844 272.8564 282.4212
##  [9] 303.5106 314.3573 285.6025 246.5272 332.2451 329.8844
## 
## $大阪府$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 192.1408 179.3816
## 44.28571 200.4347 186.2924
## 44.42857 209.1472 194.1979
## 44.57143 191.0882 175.4630
## 44.71429 161.9868 145.5927
## 44.85714 253.6541 236.2748
## 45.00000 237.6968 219.0844
## 45.14286 241.4566 219.7712
## 45.28571 258.6474 234.8982
## 45.42857 266.0204 240.4323
## 45.57143 234.1040 206.8424
## 45.71429 192.0918 163.2754
## 45.85714 275.0329 244.7467
## 46.00000 270.0124 238.3181
## 
## $大阪府$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 240.3463 253.1056
## 44.28571 253.8656 268.0080
## 44.42857 265.6266 280.5759
## 44.57143 250.1219 265.7471
## 44.71429 223.9251 240.3192
## 44.85714 319.3147 336.6940
## 45.00000 308.0160 326.6284
## 45.14286 323.3859 345.0713
## 45.28571 348.3739 372.1231
## 45.42857 362.6942 388.2822
## 45.57143 337.1009 364.3625
## 45.71429 300.9627 329.7791
## 45.85714 389.4572 419.7435
## 46.00000 389.7564 421.4507
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 55.64705 58.50710 49.92694 50.40362 44.68351 63.75054 68.51730 61.67559
##  [9] 63.03891 58.94896 59.17618 56.44954 65.53833 67.81053
## 
## $兵庫県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 46.56093 41.75102
## 44.28571 48.94483 43.88287
## 44.42857 39.91113 34.60908
## 44.57143 39.95393 34.42220
## 44.71429 33.81726 28.06502
## 44.85714 52.48312 46.51851
## 45.00000 56.86250 50.69283
## 45.14286 47.91752 40.63444
## 45.28571 48.59430 40.94779
## 45.42857 43.84899 35.85556
## 45.57143 43.44814 35.12222
## 45.71429 40.11757 31.47194
## 45.85714 48.62397 39.67005
## 46.00000 50.33318 41.08123
## 
## $兵庫県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 64.73317 69.54308
## 44.28571 68.06938 73.13134
## 44.42857 59.94275 65.24480
## 44.57143 60.85330 66.38503
## 44.71429 55.54976 61.30200
## 44.85714 75.01796 80.98257
## 45.00000 80.17209 86.34176
## 45.14286 75.43366 82.71674
## 45.28571 77.48352 85.13003
## 45.42857 74.04892 82.04235
## 45.57143 74.90421 83.23013
## 45.71429 72.78151 81.42713
## 45.85714 82.45268 91.40660
## 46.00000 85.28787 94.53982
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 12.20920 13.47891 10.87468 11.82549 12.87377 12.56376 12.50085 11.99703
##  [9] 12.13499 12.25145 12.31858 12.35855 12.24089 12.25801
## 
## $奈良県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 8.533779 6.588130
## 44.28571 9.624571 7.584209
## 44.42857 6.455177 4.115633
## 44.57143 7.327117 4.945820
## 44.71429 8.213026 5.745777
## 44.85714 7.614885 4.995110
## 45.00000 7.359932 4.638495
## 45.14286 6.669067 3.848615
## 45.28571 6.674310 3.783598
## 45.42857 6.641793 3.672218
## 45.57143 6.550340 3.496819
## 45.71429 6.435379 3.299843
## 45.85714 6.162157 2.944273
## 46.00000 6.040268 2.748797
## 
## $奈良県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 15.88462 17.83027
## 44.28571 17.33324 19.37360
## 44.42857 15.29418 17.63373
## 44.57143 16.32387 18.70517
## 44.71429 17.53451 20.00176
## 44.85714 17.51263 20.13241
## 45.00000 17.64176 20.36320
## 45.14286 17.32499 20.14544
## 45.28571 17.59568 20.48639
## 45.42857 17.86111 20.83069
## 45.57143 18.08682 21.14034
## 45.71429 18.28172 21.41725
## 45.85714 18.31961 21.53750
## 46.00000 18.47574 21.76721
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 3.647474 3.406659 2.377809 3.093987 3.216749 3.100172 2.211367 2.757253
##  [9] 2.855943 2.855943 2.855943 2.855943 2.855943 2.855943
## 
## $和歌山県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%        95%
## 44.14286  1.7463233  0.7399148
## 44.28571  1.3693577  0.2908754
## 44.42857  0.2751390 -0.8379472
## 44.57143  0.9279207 -0.2187256
## 44.71429  0.9890901 -0.1901615
## 44.85714  0.8125774 -0.3984020
## 45.00000 -0.1346321 -1.3765290
## 45.14286  0.4032289 -0.8429159
## 45.28571  0.4690684 -0.7944668
## 45.42857  0.4304661 -0.8535040
## 45.57143  0.3924686 -0.9116162
## 45.71429  0.3550483 -0.9688456
## 45.85714  0.3181797 -1.0252311
## 46.00000  0.2818392 -1.0808092
## 
## $和歌山県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 5.548625 6.555034
## 44.28571 5.443961 6.522443
## 44.42857 4.480479 5.593565
## 44.57143 5.260053 6.406700
## 44.71429 5.444409 6.623660
## 44.85714 5.387766 6.598746
## 45.00000 4.557366 5.799263
## 45.14286 5.111276 6.357421
## 45.28571 5.242818 6.506353
## 45.42857 5.281421 6.565391
## 45.57143 5.319418 6.623503
## 45.71429 5.356838 6.680732
## 45.85714 5.393707 6.737118
## 46.00000 5.430047 6.792696
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.3650069 0.9030967 1.2488977 1.3161288 0.5571755 1.2252215 1.3649167
##  [8] 1.2074972 0.8112272 1.1082506 1.1852145 0.5141497 1.0973224 1.2074720
## 
## $鳥取県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                  80%         95%
## 44.14286  0.41026341 -0.09514725
## 44.28571 -0.06448432 -0.57669078
## 44.42857  0.26864726 -0.25026601
## 44.57143  0.32337048 -0.20216401
## 44.71429 -0.44793497 -0.98000830
## 44.85714  0.20790886 -0.33062391
## 45.00000  0.33554651 -0.20936914
## 45.14286  0.17705813 -0.36842336
## 45.28571 -0.22854008 -0.77895962
## 45.42857  0.05923813 -0.49607555
## 45.57143  0.12703748 -0.43312759
## 45.71429 -0.55311310 -1.11808789
## 45.85714  0.02105055 -0.54869336
## 46.00000  0.12226582 -0.45220763
## 
## $鳥取県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.319750 2.825161
## 44.28571 1.870678 2.382884
## 44.42857 2.229148 2.748062
## 44.57143 2.308887 2.834422
## 44.71429 1.562286 2.094359
## 44.85714 2.242534 2.781067
## 45.00000 2.394287 2.939202
## 45.14286 2.237936 2.783418
## 45.28571 1.850994 2.401414
## 45.42857 2.157263 2.712577
## 45.57143 2.243391 2.803557
## 45.71429 1.581412 2.146387
## 45.85714 2.173594 2.743338
## 46.00000 2.292678 2.867152
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.4668874 0.4668874 0.4668874 0.4668874 0.4668874 0.4668874 0.4668874
##  [8] 0.4668874 0.4668874 0.4668874 0.4668874 0.4668874 0.4668874 0.4668874
## 
## $島根県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -6.348624 -9.956538
## 44.28571 -6.348624 -9.956538
## 44.42857 -6.348624 -9.956538
## 44.57143 -6.348624 -9.956538
## 44.71429 -6.348624 -9.956538
## 44.85714 -6.348624 -9.956538
## 45.00000 -6.348624 -9.956538
## 45.14286 -6.348624 -9.956538
## 45.28571 -6.348624 -9.956538
## 45.42857 -6.348624 -9.956538
## 45.57143 -6.348624 -9.956538
## 45.71429 -6.348624 -9.956538
## 45.85714 -6.348624 -9.956538
## 46.00000 -6.348624 -9.956538
## 
## $島根県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 7.282399 10.89031
## 44.28571 7.282399 10.89031
## 44.42857 7.282399 10.89031
## 44.57143 7.282399 10.89031
## 44.71429 7.282399 10.89031
## 44.85714 7.282399 10.89031
## 45.00000 7.282399 10.89031
## 45.14286 7.282399 10.89031
## 45.28571 7.282399 10.89031
## 45.42857 7.282399 10.89031
## 45.57143 7.282399 10.89031
## 45.71429 7.282399 10.89031
## 45.85714 7.282399 10.89031
## 46.00000 7.282399 10.89031
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 6.483216 6.311020 6.253643 6.234524 6.228154 6.226031 6.225324 6.225088
##  [9] 6.225010 6.224984 6.224975 6.224972 6.224971 6.224971
## 
## $岡山県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%       95%
## 44.14286 3.884482 2.5087956
## 44.28571 3.416596 1.8843803
## 44.42857 3.241677 1.6472386
## 44.57143 3.145384 1.5100915
## 44.71429 3.073981 1.4042626
## 44.85714 3.011332 1.3095738
## 45.00000 2.952245 1.2195820
## 45.14286 2.894987 1.1321382
## 45.28571 2.838956 1.0464872
## 45.42857 2.783922 0.9623346
## 45.57143 2.729784 0.8795413
## 45.71429 2.676480 0.7980223
## 45.85714 2.623969 0.7177137
## 46.00000 2.572213 0.6385607
## 
## $岡山県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 9.081949 10.45764
## 44.28571 9.205443 10.73766
## 44.42857 9.265608 10.86005
## 44.57143 9.323665 10.95896
## 44.71429 9.382327 11.05204
## 44.85714 9.440730 11.14249
## 45.00000 9.498403 11.23107
## 45.14286 9.555189 11.31804
## 45.28571 9.611063 11.40353
## 45.42857 9.666045 11.48763
## 45.57143 9.720166 11.57041
## 45.71429 9.773463 11.65192
## 45.85714 9.825973 11.73223
## 46.00000 9.877728 11.81138
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.970722 1.860515 1.899368 1.949688 1.918411 1.898298 1.919163 1.925434
##  [9] 1.913026 1.912319 1.919059 1.918009 1.914651 1.915919
## 
## $広島県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%        95%
## 44.14286 -1.557126  -3.424655
## 44.28571 -2.053494  -4.125444
## 44.42857 -2.206422  -4.379897
## 44.57143 -2.761214  -5.255015
## 44.71429 -3.335123  -6.116176
## 44.85714 -3.640279  -6.572225
## 45.00000 -3.925460  -7.019417
## 45.14286 -4.300774  -7.596730
## 45.28571 -4.634903  -8.101167
## 45.42857 -4.906770  -8.516578
## 45.57143 -5.185336  -8.946175
## 45.71429 -5.473410  -9.386191
## 45.85714 -5.738018  -9.789097
## 46.00000 -5.986203 -10.169333
## 
## $広島県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%       95%
## 44.14286 5.498570  7.366100
## 44.28571 5.774523  7.846473
## 44.42857 6.005159  8.178633
## 44.57143 6.660590  9.154391
## 44.71429 7.171946  9.952998
## 44.85714 7.436875 10.368821
## 45.00000 7.763787 10.857744
## 45.14286 8.151643 11.447598
## 45.28571 8.460955 11.927219
## 45.42857 8.731408 12.341216
## 45.57143 9.023454 12.784293
## 45.71429 9.309429 13.222210
## 45.85714 9.567321 13.618400
## 46.00000 9.818040 14.001171
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 2.271223 2.271223 2.271223 2.271223 2.271223 2.271223 2.271223 2.271223
##  [9] 2.271223 2.271223 2.271223 2.271223 2.271223 2.271223
## 
## $山口県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                   80%        95%
## 44.14286  0.351311711 -0.6650278
## 44.28571  0.278122726 -0.7769607
## 44.42857  0.207527760 -0.8849263
## 44.57143  0.139269111 -0.9893189
## 44.71429  0.073129116 -1.0904713
## 44.85714  0.008921946 -1.1886677
## 45.00000 -0.053512554 -1.2841530
## 45.14286 -0.114313571 -1.3771402
## 45.28571 -0.173602978 -1.4678155
## 45.42857 -0.231488209 -1.5563433
## 45.57143 -0.288064543 -1.6428694
## 45.71429 -0.343416949 -1.7275236
## 45.85714 -0.397621580 -1.8104224
## 46.00000 -0.450747008 -1.8916707
## 
## $山口県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 4.191134 5.207473
## 44.28571 4.264323 5.319406
## 44.42857 4.334918 5.427372
## 44.57143 4.403176 5.531764
## 44.71429 4.469316 5.632917
## 44.85714 4.533523 5.731113
## 45.00000 4.595958 5.826598
## 45.14286 4.656759 5.919586
## 45.28571 4.716048 6.010261
## 45.42857 4.773934 6.098789
## 45.57143 4.830510 6.185315
## 45.71429 4.885862 6.269969
## 45.85714 4.940067 6.352868
## 46.00000 4.993192 6.434116
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.4652409 1.1679230 0.1355894 0.5320060 0.7068681 0.8249196 0.5035317
##  [8] 0.5954733 0.1970962 0.7573177 0.6600168 0.5145609 0.3835087 0.6662705
## 
## $徳島県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286 -1.2696774 -2.188088
## 44.28571 -0.6491567 -1.611061
## 44.42857 -1.7002801 -2.672131
## 44.57143 -1.3224629 -2.304159
## 44.71429 -1.1660155 -2.157460
## 44.85714 -1.0661994 -2.067297
## 45.00000 -1.4056486 -2.416308
## 45.14286 -1.4132333 -2.476578
## 45.28571 -1.8462871 -2.927989
## 45.42857 -1.3104658 -2.405084
## 45.57143 -1.4318824 -2.539267
## 45.71429 -1.6011792 -2.721184
## 45.85714 -1.7558066 -2.888292
## 46.00000 -1.4963630 -2.641192
## 
## $徳島県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.200159 3.118569
## 44.28571 2.985003 3.946906
## 44.42857 1.971459 2.943309
## 44.57143 2.386475 3.368171
## 44.71429 2.579752 3.571196
## 44.85714 2.716039 3.717137
## 45.00000 2.412712 3.423371
## 45.14286 2.604180 3.667525
## 45.28571 2.240480 3.322181
## 45.42857 2.825101 3.919720
## 45.57143 2.751916 3.859301
## 45.71429 2.630301 3.750306
## 45.85714 2.522824 3.655309
## 46.00000 2.828904 3.973733
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.8656491 0.5580087 0.5580087 0.5580087 0.5580087 0.5580087 0.5580087
##  [8] 0.5580087 0.5580087 0.5580087 0.5580087 0.5580087 0.5580087 0.5580087
## 
## $香川県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286 -0.4979687 -1.219824
## 44.28571 -0.8666601 -1.620834
## 44.42857 -0.8706898 -1.626997
## 44.57143 -0.8747083 -1.633143
## 44.71429 -0.8787155 -1.639271
## 44.85714 -0.8827115 -1.645383
## 45.00000 -0.8866965 -1.651477
## 45.14286 -0.8906705 -1.657555
## 45.28571 -0.8946337 -1.663616
## 45.42857 -0.8985860 -1.669661
## 45.57143 -0.9025277 -1.675689
## 45.71429 -0.9064588 -1.681701
## 45.85714 -0.9103793 -1.687697
## 46.00000 -0.9142894 -1.693677
## 
## $香川県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.229267 2.951123
## 44.28571 1.982677 2.736852
## 44.42857 1.986707 2.743015
## 44.57143 1.990726 2.749160
## 44.71429 1.994733 2.755289
## 44.85714 1.998729 2.761400
## 45.00000 2.002714 2.767495
## 45.14286 2.006688 2.773572
## 45.28571 2.010651 2.779633
## 45.42857 2.014603 2.785678
## 45.57143 2.018545 2.791706
## 45.71429 2.022476 2.797718
## 45.85714 2.026397 2.803714
## 46.00000 2.030307 2.809694
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.2933581 0.3758607 0.3761888 0.3017884 0.3812002 0.3113277 0.3773469
##  [8] 0.3496181 0.4026641 0.3631385 0.4031285 0.3672712 0.4009032 0.3698957
## 
## $愛媛県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -1.334359 -2.196021
## 44.28571 -1.298718 -2.185186
## 44.42857 -1.325670 -2.226580
## 44.57143 -1.400706 -2.301952
## 44.71429 -1.325914 -2.229605
## 44.85714 -1.398017 -2.302889
## 45.00000 -1.334420 -2.240575
## 45.14286 -1.365196 -2.272964
## 45.28571 -1.316363 -2.226361
## 45.42857 -1.356264 -2.266460
## 45.57143 -1.317313 -2.228060
## 45.71429 -1.353787 -2.264860
## 45.85714 -1.320768 -2.232165
## 46.00000 -1.352273 -2.263934
## 
## $愛媛県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 1.921076 2.782737
## 44.28571 2.050439 2.936907
## 44.42857 2.078048 2.978958
## 44.57143 2.004283 2.905529
## 44.71429 2.088314 2.992006
## 44.85714 2.020672 2.925545
## 45.00000 2.089114 2.995269
## 45.14286 2.064433 2.972201
## 45.28571 2.121691 3.031690
## 45.42857 2.082541 2.992737
## 45.57143 2.123570 3.034317
## 45.71429 2.088329 2.999402
## 45.85714 2.122574 3.033972
## 46.00000 2.092065 3.003726
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.1552617 0.1778486 0.1987066 0.2179682 0.2357555 0.2521814 0.2673500
##  [8] 0.2813577 0.2942932 0.3062386 0.3172697 0.3274564 0.3368635 0.3455506
## 
## $高知県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -1.550690 -2.453766
## 44.28571 -1.549915 -2.464538
## 44.42857 -1.547443 -2.471799
## 44.57143 -1.543709 -2.476284
## 44.71429 -1.539056 -2.478584
## 44.85714 -1.533754 -2.479171
## 45.00000 -1.528017 -2.478427
## 45.14286 -1.522014 -2.476661
## 45.28571 -1.515877 -2.474123
## 45.42857 -1.509708 -2.471012
## 45.57143 -1.503589 -2.467494
## 45.71429 -1.497581 -2.463697
## 45.85714 -1.491730 -2.459729
## 46.00000 -1.486070 -2.455671
## 
## $高知県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 1.861213 2.764290
## 44.28571 1.905612 2.820235
## 44.42857 1.944856 2.869212
## 44.57143 1.979645 2.912221
## 44.71429 2.010567 2.950095
## 44.85714 2.038117 2.983534
## 45.00000 2.062717 3.013127
## 45.14286 2.084729 3.039377
## 45.28571 2.104463 3.062709
## 45.42857 2.122185 3.083490
## 45.57143 2.138129 3.102033
## 45.71429 2.152494 3.118610
## 45.85714 2.165457 3.133456
## 46.00000 2.177171 3.146772
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 18.16853 18.53692 15.12333 14.03612 15.63377 20.69753 21.63305 20.27504
##  [9] 19.57606 17.03094 16.48787 17.69633 21.39267 21.88335
## 
## $福岡県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                  80%        95%
## 44.14286   4.5637259  -2.638223
## 44.28571   2.5508823  -5.911615
## 44.42857  -2.1533193 -11.299024
## 44.57143  -3.9465068 -13.465937
## 44.71429  -2.9650543 -12.810678
## 44.85714   1.2001980  -9.121065
## 45.00000   0.7800772 -10.258822
## 45.14286  -3.1884990 -15.609344
## 45.28571  -5.7335487 -19.131646
## 45.42857  -9.5085480 -23.557705
## 45.57143 -10.8540746 -25.328028
## 45.71429 -10.3433399 -25.186643
## 45.85714  -7.5173858 -22.821446
## 46.00000  -8.2098462 -24.140223
## 
## $福岡県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 31.77334 38.97529
## 44.28571 34.52296 42.98546
## 44.42857 32.39998 41.54568
## 44.57143 32.01876 41.53819
## 44.71429 34.23260 44.07822
## 44.85714 40.19486 50.51612
## 45.00000 42.48603 53.52493
## 45.14286 43.73857 56.15941
## 45.28571 44.88567 58.28377
## 45.42857 43.57043 57.61959
## 45.57143 43.82982 58.30378
## 45.71429 45.73599 60.57930
## 45.85714 50.30273 65.60679
## 46.00000 51.97655 67.90693
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 1.530147 1.530147 1.530147 1.530147 1.530147 1.530147 1.530147 1.530147
##  [9] 1.530147 1.530147 1.530147 1.530147 1.530147 1.530147
## 
## $佐賀県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286 -0.4720964 -1.532020
## 44.28571 -0.5474691 -1.647293
## 44.42857 -0.6202015 -1.758528
## 44.57143 -0.6905531 -1.866121
## 44.71429 -0.7587434 -1.970409
## 44.85714 -0.8249601 -2.071679
## 45.00000 -0.8893652 -2.170178
## 45.14286 -0.9520999 -2.266122
## 45.28571 -1.0132877 -2.359701
## 45.42857 -1.0730376 -2.451081
## 45.57143 -1.1314466 -2.540409
## 45.71429 -1.1886010 -2.627819
## 45.85714 -1.2445783 -2.713429
## 46.00000 -1.2994485 -2.797346
## 
## $佐賀県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 3.532391 4.592315
## 44.28571 3.607764 4.707588
## 44.42857 3.680497 4.818823
## 44.57143 3.750848 4.926416
## 44.71429 3.819038 5.030704
## 44.85714 3.885255 5.131974
## 45.00000 3.949660 5.230473
## 45.14286 4.012395 5.326417
## 45.28571 4.073583 5.419996
## 45.42857 4.133333 5.511376
## 45.57143 4.191742 5.600704
## 45.71429 4.248896 5.688114
## 45.85714 4.304873 5.773724
## 46.00000 4.359743 5.857641
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1]  1.939689e-01  5.393244e-01  4.546823e-02  1.771589e-05  5.693942e-05
##  [6]  1.966336e-05  2.055972e-05  1.247114e-01  3.667315e-01 -2.997562e-02
## [11]  1.034931e-05 -1.519258e-05  8.098007e-06  5.343952e-06
## 
## $長崎県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -1.948469 -3.082607
## 44.28571 -1.844742 -3.106790
## 44.42857 -2.829708 -4.351734
## 44.57143 -3.163612 -4.838336
## 44.71429 -3.487104 -5.333095
## 44.85714 -3.754558 -5.742111
## 45.00000 -4.017708 -6.144564
## 45.14286 -4.284549 -6.618670
## 45.28571 -4.330266 -6.816706
## 45.42857 -5.031964 -7.679857
## 45.57143 -5.272795 -8.064050
## 45.71429 -5.538448 -8.470317
## 45.85714 -5.787926 -8.851873
## 46.00000 -6.029051 -9.220641
## 
## $長崎県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.336407 3.470545
## 44.28571 2.923390 4.185439
## 44.42857 2.920644 4.442670
## 44.57143 3.163647 4.838372
## 44.71429 3.487218 5.333209
## 44.85714 3.754597 5.742151
## 45.00000 4.017749 6.144605
## 45.14286 4.533972 6.868093
## 45.28571 5.063728 7.550169
## 45.42857 4.972013 7.619905
## 45.57143 5.272816 8.064070
## 45.71429 5.538417 8.470286
## 45.85714 5.787942 8.851889
## 46.00000 6.029062 9.220652
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 4.604614 4.944512 4.890286 4.890286 4.890286 4.890286 4.890286 4.890286
##  [9] 4.890286 4.890286 4.890286 4.890286 4.890286 4.890286
## 
## $熊本県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286  0.4057157 -1.817047
## 44.28571 -0.6428004 -3.600545
## 44.42857 -0.8509625 -3.890196
## 44.57143 -1.1907969 -4.409928
## 44.71429 -1.5126199 -4.902113
## 44.85714 -1.8190239 -5.370718
## 45.00000 -2.1120332 -5.818837
## 45.14286 -2.3932646 -6.248943
## 45.28571 -2.6640336 -6.663049
## 45.42857 -2.9254276 -7.062816
## 45.57143 -3.1783579 -7.449640
## 45.71429 -3.4235969 -7.824700
## 45.85714 -3.6618064 -8.189010
## 46.00000 -3.8935582 -8.543444
## 
## $熊本県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%      95%
## 44.14286  8.803513 11.02628
## 44.28571 10.531825 13.48957
## 44.42857 10.631534 13.67077
## 44.57143 10.971368 14.19050
## 44.71429 11.293191 14.68268
## 44.85714 11.599595 15.15129
## 45.00000 11.892604 15.59941
## 45.14286 12.173836 16.02951
## 45.28571 12.444605 16.44362
## 45.42857 12.705999 16.84339
## 45.57143 12.958929 17.23021
## 45.71429 13.204168 17.60527
## 45.85714 13.442377 17.96958
## 46.00000 13.674129 18.32402
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.6097693 0.7557490 0.6295461 0.4502471 0.4536713 0.4569132 0.4599824
##  [8] 0.4628881 0.4656390 0.4682433 0.4707090 0.4730433 0.4752532 0.4773454
## 
## $大分県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286 -0.7684228 -1.497994
## 44.28571 -0.8330389 -1.674092
## 44.42857 -1.1294314 -2.060578
## 44.57143 -1.3732498 -2.338551
## 44.71429 -1.3792894 -2.349600
## 44.85714 -1.3844887 -2.359268
## 45.00000 -1.3889526 -2.367720
## 45.14286 -1.3927729 -2.375100
## 45.28571 -1.3960297 -2.381538
## 45.42857 -1.3987937 -2.387143
## 45.57143 -1.4011266 -2.392017
## 45.71429 -1.4030828 -2.396244
## 45.85714 -1.4047101 -2.399903
## 46.00000 -1.4060506 -2.403060
## 
## $大分県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 1.987961 2.717532
## 44.28571 2.344537 3.185590
## 44.42857 2.388524 3.319670
## 44.57143 2.273744 3.239045
## 44.71429 2.286632 3.256943
## 44.85714 2.298315 3.273094
## 45.00000 2.308917 3.287685
## 45.14286 2.318549 3.300877
## 45.28571 2.327308 3.312816
## 45.42857 2.335280 3.323630
## 45.57143 2.342545 3.333434
## 45.71429 2.349169 3.342330
## 45.85714 2.355217 3.350409
## 46.00000 2.360741 3.357751
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 0.050622541 0.008979163 0.008979163 0.008979163 0.008979163 0.008979163
##  [7] 0.008979163 0.008979163 0.008979163 0.008979163 0.008979163 0.008979163
## [13] 0.008979163 0.008979163
## 
## $宮崎県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%       95%
## 44.14286 -2.653191 -4.084504
## 44.28571 -2.809758 -4.301907
## 44.42857 -3.136321 -4.801342
## 44.57143 -3.432031 -5.253591
## 44.71429 -3.704265 -5.669938
## 44.85714 -3.957861 -6.057779
## 45.00000 -4.196191 -6.422274
## 45.14286 -4.421720 -6.767190
## 45.28571 -4.636312 -7.095380
## 45.42857 -4.841419 -7.409065
## 45.57143 -5.038198 -7.710012
## 45.71429 -5.227588 -7.999659
## 45.85714 -5.410363 -8.279189
## 46.00000 -5.587172 -8.549595
## 
## $宮崎県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 2.754436 4.185749
## 44.28571 2.827716 4.319865
## 44.42857 3.154279 4.819301
## 44.57143 3.449989 5.271550
## 44.71429 3.722224 5.687896
## 44.85714 3.975819 6.075737
## 45.00000 4.214149 6.440232
## 45.14286 4.439678 6.785148
## 45.28571 4.654270 7.113338
## 45.42857 4.859377 7.427023
## 45.57143 5.056156 7.727971
## 45.71429 5.245546 8.017617
## 45.85714 5.428321 8.297148
## 46.00000 5.605130 8.567553
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 4.454917 3.890233 4.094237 3.779769 3.756517 3.756517 3.756517 3.756517
##  [9] 3.756517 3.756517 3.756517 3.756517 3.756517 3.756517
## 
## $鹿児島県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                 80%       95%
## 44.14286  0.4604760 -1.654053
## 44.28571 -0.8703871 -3.390507
## 44.42857 -0.9261772 -3.583824
## 44.57143 -1.3739160 -4.102112
## 44.71429 -1.4552608 -4.214209
## 44.85714 -1.4690936 -4.235365
## 45.00000 -1.4828898 -4.256464
## 45.14286 -1.4966499 -4.277508
## 45.28571 -1.5103739 -4.298497
## 45.42857 -1.5240623 -4.319432
## 45.57143 -1.5377153 -4.340313
## 45.71429 -1.5513332 -4.361139
## 45.85714 -1.5649163 -4.381913
## 46.00000 -1.5784647 -4.402633
## 
## $鹿児島県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 8.449358 10.56389
## 44.28571 8.650853 11.17097
## 44.42857 9.114650 11.77230
## 44.57143 8.933454 11.66165
## 44.71429 8.968295 11.72724
## 44.85714 8.982127 11.74840
## 45.00000 8.995924 11.76950
## 45.14286 9.009684 11.79054
## 45.28571 9.023408 11.81153
## 45.42857 9.037096 11.83247
## 45.57143 9.050749 11.85335
## 45.71429 9.064367 11.87417
## 45.85714 9.077950 11.89495
## 46.00000 9.091498 11.91567
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##  [1] 31.22550 30.21109 32.49802 32.14252 30.04789 31.31470 34.11675 32.43302
##  [9] 32.43302 32.43302 32.43302 32.43302 32.43302 32.43302
## 
## $沖縄県$lower
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##                80%        95%
## 44.14286 19.508993 13.3066500
## 44.28571 17.401630 10.6207099
## 44.42857 18.681788 11.3679172
## 44.57143 17.388061  9.5775213
## 44.71429 14.411392  6.1339322
## 44.85714 14.843338  6.1239251
## 45.00000 16.850839  7.7108174
## 45.14286 13.430666  3.3714261
## 45.28571 12.383009  1.7691737
## 45.42857 11.387441  0.2465834
## 45.57143 10.436887 -1.2071633
## 45.71429  9.525743 -2.6006376
## 45.85714  8.649479 -3.9407672
## 46.00000  7.804373 -5.2332465
## 
## $沖縄県$upper
## Time Series:
## Start = c(44, 2) 
## End = c(46, 1) 
## Frequency = 7 
##               80%      95%
## 44.14286 42.94200 49.14434
## 44.28571 43.02056 49.80148
## 44.42857 46.31425 53.62812
## 44.57143 46.89698 54.70752
## 44.71429 45.68438 53.96184
## 44.85714 47.78607 56.50548
## 45.00000 51.38266 60.52268
## 45.14286 51.43538 61.49462
## 45.28571 52.48303 63.09687
## 45.42857 53.47860 64.61946
## 45.57143 54.42916 66.07321
## 45.71429 55.34030 67.46668
## 45.85714 56.21656 68.80681
## 46.00000 57.06167 70.09929